An air turbulence analysis system and method includes an air turbulence control unit that is configured to receive a position signal from an aircraft within an air space. The air turbulence control unit determines a location of air turbulence within the air space based on the position signal. In at least one embodiment, the position signal is an automatic dependent surveillance-broadcast (ADS-B) signal.

Systems and methods for detection of clear air turbulence are provided. One system includes an image capture device suitable to capture one or more images of an optical phenomenon caused by non-horizontally oriented ice crystals. The system also includes a computer processor configured to receive the one or more images from the image capture device, analyze the one or more images by comparing one or more characteristics of the one or more images to one or more threshold values, and determine based on the comparing, an occurrence of clear air turbulence.

A wind forecasting system configured to generate forecasted wind conditions for a time interval based on wind data derived from flight log data collected from a plurality of aerial vehicles operating in a first area. The forecasted wind conditions can be used by an aerial route management system to generate a flight plan for an aerial vehicle from a start location to an end location in the first area during the time interval.

Systems, apparatuses, and methods for acquiring information regarding weather conditions along a flightpath from other aircraft and incorporating that information into a flight path optimization and planning system. The system and methods may acquire substantially real-time information from pilots or other crew who have encountered a weather system or event (e.g., a wind direction or speed measurement, an observation of a storm or lightning, an observation of a difficulty in controlling an aircraft, unexpected excessive turbulence, etc.) and share that information with other airborne pilots or crew, either directly or using a ground-based server. The server may receive and process the acquired information and determine which aircraft may be likely to encounter or be impacted by a weather system or event for which it has received additional weather-related data and information.

A system for an aircraft includes a user interface device and a controller. The controller is configured to receive a present location of the aircraft, a present location of one or more wind vortices, a strength of the one or more wind vortices, and a desired ending point of the aircraft. The desired ending point of the aircraft is received from the user interface device. The controller is configured to define an optimization problem, and determine a solution to the optimization problem. The solution to the optimization problem includes a flight path and steering angle values to achieve the flight path. The flight path results in a minimum time to reach the desired ending point. The controller is configured to cause the user interface device to display a map including the flight path.

[Object] To provide a remote airflow observation device, a remote airflow observation method, and a program that are capable of eliminating the influence of scattered waves from the ground or some object and improving the reliability of airflow observation.

[Solving Means] A Doppler Lidar 100 includes: a measurement unit 110 that radiates transmission light including pulse-form laser light, receives reflected light of the radiated transmission light as reception light, and outputs a received signal for calculating a wind speed value on the basis of the transmission light and the reception light; and a signal processor 8 that performs processing for distributing the received signal to range bins by time-dividing the received signal and for removing a signal derived from a hard target from the received signal.

A method for estimating a characteristic atmospheric turbulence parameter, the method including the steps of: training a machine learning model using, as learning data, values of the characteristic parameter with which are associated optical speckle images corresponding to defocused images of one or more stars, and using the trained learning model to estimate the characteristic parameter from input data containing one or more optical speckle images acquired by at least one measuring telescope, where the speckle images correspond to defocused images of one or more stars observed in real conditions by the telescope.

A control apparatus includes a reception section that receives a wind speed vector measured at any time point by at least one external anemometer, a wind-power prediction section that, on the basis of the received wind speed vector, predicts a wind power to be applied to the mobile body after elapse of a predetermined time period, and a control section that controls driving of the mobile body on the basis of the predicted wind power.

A method of management of a flight of an aircraft is provided. The method includes receiving a data stream from an automatic dependent surveillance broadcast (ADS-B) receiver during the flight, and the data stream includes an ADS-B message, a geographic position of the aircraft, and attitude and heading reference system (AHRS) data. The method includes accessing data that indicates a weight of the aircraft, identifying a force event experienced by the aircraft based on the geographic position, the AHRS data, and the weight of the aircraft, and classifying the force event by level of the force event, and by type of the force event as a turbulence event or a non-turbulence event. And the method includes outputting a report that indicates the force event as classified, at least when the force event is classified as a turbulence event.

There is provided an aircraft flight control system comprising: a computing arrangement including an input interface and an output interface; wherein in operation the computing arrangement executes instructions to provide indications related to an estimated atmospheric contamination risk to at least one aircraft at selected locations and altitudes or pressures, by (i) receiving at least one aircraft flight plan data from the input interface; wherein at least one aircraft flight plan data includes at least one of time, a pressure or an altitude, a trajectory and a location representing at least one aircraft flight; (ii) determining the estimated atmospheric contamination risk using a measure of the at least one atmospheric contaminant for the at least one aircraft flight based upon a location, an altitude or pressure, a trajectory and a time information extracted from the at least one aircraft flight plan data; and (iii) providing, via the output interface, a resultant indication related to the estimated atmospheric contamination risk to the at least one aircraft.